1. Tight Coupling to Legacy Path
def label(score_df, ctx: Dict[str, Any], score_out: Dict[str, Any], cfg: Dict[str, Any]):
basis_train: Optional[pd.DataFrame] = ctx.get("regime_basis_train")
# ... modern path
return _legacy_label(score_df, ctx, out, cfg) # Fallback
_legacy_label() for backward compatibility2. State Machine vs. Vectorized Operations
class _StateMachine:
def update(self, roc_value: float, changed: bool) -> str:
# Iterative processing of each sample
3. Configuration Schema Validation
_REGIME_CONFIG_SCHEMA = {
"regimes.auto_k.k_min": (int, 2, 20, "Minimum clusters"),
# Only 4 parameters validated
}
1. Degenerate k=1 Handling
def _fit_kmeans_scaled(...) -> Tuple[...]:
for k in range(max(2, k_min), k_max + 1):
# Loop starts at k=2
if best_model_eval is None:
fallback_k = max(1, min(k_min, n_samples)) # Can return k=1
fallback_k = max(2, ...) and mark as quality failure2. Transition Smoothing Priority Logic
def _candidate_score(label: int, segment_start: int, segment_end: int) -> Tuple[int, int]:
health_rank = _HEALTH_PRIORITY.get(health, _HEALTH_PRIORITY["unknown"])
# Returns (health_rank, -run_length)
(-run_length, health_rank) to prioritize persistence3. Duration Estimation Edge Cases
def _compute_sample_durations(index: pd.Index) -> np.ndarray:
if isinstance(index, pd.DatetimeIndex):
diffs = np.diff(values).astype(np.float64) / 1e9
durations[:-1] = np.where(np.isfinite(diffs) & (diffs >= 0), diffs, fallback)
durations[-1] = fallback if fallback > 0 else (diffs[-1] if diffs.size else 0.0)
fallback or diffs[-1]diffs[-1] is negative or inf, duration becomes invaliddwell_seconds and stability_score corrupteddurations[-1] = fallback if fallback > 0 and np.isfinite(fallback) else 0.04. Alignment Dimension Mismatch
def align_regime_labels(new_model: RegimeModel, prev_model: RegimeModel) -> RegimeModel:
if new_centers.shape[1] != prev_centers.shape[1]:
Console.warn(f"Feature dimension mismatch...")
return new_model # Returns unaligned model
5. Silhouette Sampling Bias
if n_samples > max_eval_samples:
eval_idx = rng.choice(n_samples, size=max_eval_samples, replace=False)
X_eval = X_scaled[eval_idx]
6. Health Label Race Condition
def smooth_transitions(..., health_map: Optional[Dict[int, str]] = None):
# Uses health_map during smoothing
def update_health_labels(...) -> Dict[int, Dict[str, Any]]:
# Computes health labels after smoothing
health_map, second run uses stale labelsupdate_health_labels()_finite_impute_inplace() handles NaN, inf, -inf_robust_scale_clip() uses IQR instead of std for outlier resistance_validate_regime_inputs() checks for empty data, non-numeric columns, low variance7. Implicit Feature Scaling
def build_feature_basis(...):
basis_scaler = StandardScaler()
basis_scaler.fit(train_basis.values)
# Scales entire basis (PCA + raw tags)
8. Zero-Variance Detection
def _validate_regime_inputs(df: pd.DataFrame, name: str = "train_basis") -> List[str]:
variances = numeric.var(axis=0)
low_var_cols = variances[variances <= 1e-6].index.tolist()
9. Missing Data Propagation
def _read_scores_csv(p: Path) -> pd.DataFrame:
df["timestamp"] = _to_datetime_mixed(df["timestamp"])
return df[~df.index.isna()] # Drops invalid timestamps
10. Repeated Memory Copies
def _finite_impute_inplace(X: np.ndarray) -> np.ndarray:
X = _as_f32(X) # Copy to float32
# ... modifications
return X
_as_f32()_finite_impute()11. Quadratic Transition Counting
for seg_idx, (label_value, start_idx, end_idx) in enumerate(segments):
if seg_idx > 0:
prev_label, _, _ = segments[seg_idx - 1] # O(1) lookup
# But builds transition dict incrementally
Counter from collections, update once12. Unoptimized Smoothing
def smooth_labels(labels: np.ndarray, passes: int = 1, window: Optional[int] = None):
for _ in range(iterations):
for i in range(1, len(smoothed) - 1): # O(n*passes)
13. JSON Serialization Overhead
def save_regime_model(model: RegimeModel, models_dir: Path):
metadata = {
"stats": {
str(k): {kk: (float(vv) if isinstance(vv, ...) else str(vv)) ...}
}
}
orjson or msgpack for 5-10x speedup14. Silent Persistence Failures
def _persist_regime_error(e: Exception, models_dir: Path):
err_file = models_dir / "regime_persist.errors.txt"
with err_file.open("w", encoding="utf-8") as f:
f.write(f"Error type: {type(e).__name__}\n\n{traceback.format_exc()}")
save_regime_model() with try/except calling this handler15. Model Version Mismatch Handling
def load_regime_model(models_dir: Path) -> Optional[RegimeModel]:
version = meta.get("model_version")
if version and version != REGIME_MODEL_VERSION:
Console.warn(f"Cached model version {version} mismatches...")
return None
ModelVersionError with upgrade instructions16. Hash Collision Risk
def fit_regime_model(..., train_hash: Optional[int], ...):
regime_model.train_hash = train_hash
hash() for data fingerprintinghashlib.md5() or xxhash for stable hashing17. Quality Metric Explosion
out["regime_quality_ok"] = quality_ok
out["regime_quality_notes"] = list(regime_model.meta.get("quality_notes", []))
out["regime_sweep_scores"] = list(regime_model.meta.get("quality_sweep", []))
regime_quality_score (0-100) combining all factors18. Missing Convergence Metrics
best_model = MiniBatchKMeans(
n_clusters=best_k,
batch_size=...,
n_init=20,
random_state=random_state,
)
best_model.fit(X_scaled)
best_model.n_iter_ and best_model.inertia_19. Edge Case Validation
def _cfg_get(cfg: Dict[str, Any], path: str, default: Any) -> Any:
for part in path.split("."):
if not isinstance(cur, dict) or part not in cur:
return default
cur = cur[part]
return cur
20. Smoothing Integration Tests
labels = smooth_labels(labels, passes=passes)
labels = smooth_transitions(labels, timestamps=ts_pred, ...)
21. Redundant Feature Scaling
# In build_feature_basis():
basis_scaler.fit(train_basis.values)
train_basis = pd.DataFrame(basis_scaler.transform(train_basis.values), ...)
# In fit_regime_model():
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
pre_scaled=True flag to fit_regime_model()22. Persistence Logic Duplication
def save_regime_model(model: RegimeModel, models_dir: Path):
# Saves to joblib + json
def regime_model_to_state(model: RegimeModel, ...):
# Saves to database-compatible state
regime_model_to_state() call save_regime_model() internally23. Path Traversal (Low Risk)
def _read_episodes_csv(p: Path) -> pd.DataFrame:
if not p.exists():
return pd.DataFrame(columns=["start_ts", "end_ts"])
df = pd.read_csv(p, ...)
Path objects from caller without validationctx.run_dir could read arbitrary filesctx.run_dir to safe locationsassert p.is_relative_to(safe_base_dir)24. Resource Exhaustion (Medium Risk)
def _fit_kmeans_scaled(...):
for k in range(k_min, k_max + 1):
km_eval = MiniBatchKMeans(...)
km_eval.fit(X_eval) # Unbounded k_max
k_max=40 (default)_persist_regime_error() in save path - Enable failure diagnosis| Category | Score | Notes |
|---|---|---|
| Architecture | 7/10 | Modular design, but legacy coupling |
| Correctness | 6/10 | Multiple logic errors in edge cases |
| Performance | 6/10 | Bottlenecks in state machine, I/O |
| Robustness | 8/10 | Good input validation, needs error handling |
| Maintainability | 7/10 | Clear structure, some duplication |
| Testing | 4/10 | Missing edge case and integration tests |
| Security | 8/10 | Low-risk issues only |
| Documentation | 6/10 | Docstrings present, missing architecture docs |
Overall: 6.5/10 - Solid foundation with critical correctness issues requiring immediate attention.